A strategy to reduce the false‐positive rate after low‐dose computed tomography in lung cancer screening: A multicenter prospective cohort study

Abstract

Abstract Background The ability of lung cancer screening to manage pulmonary nodules was limited because of the high false‐positive rate in the current mainstream screening method, low‐dose computed tomography (LDCT). We aimed to reduce overdiagnosis in Chinese population. Methods Lung cancer risk prediction models were constructed using data from a population‐based cohort in China. Independent clinical data from two programs performed in Beijing and Shandong, respectively, were used as the external validation set. Multivariable logistic regression models were used to estimate the probability of lung cancer incidence in the whole population and in smokers and nonsmokers. Results In our cohort, 1,016,740 participants were enrolled between 2013 and 2018. Of 79,581 who received LDCT screening, 5165 participants with suspected pulmonary nodules were allocated into the training set, of which, 149 lung cancer cases were diagnosed. In the validation set, 1815 patients were included, and 800 developed lung cancer. The ages of patients and radiologic factors of nodules (calcification, density, mean diameter, edge, and pleural involvement) were included in our model. The area under the curve (AUC) values of the model were 0.868 (95% CI: 0.839–0.894) in the training set and 0.751 (95% CI: 0.727–0.774) in the validation set. The sensitivity and specificity were 70.5% and 70.9%, respectively, which could reduce the 68.8% false‐positive rate in simulated LDCT screening. There was no substantial difference between smokers' and nonsmokers' prediction models. Conclusion Our models could facilitate the diagnosis of suspected pulmonary nodules, effectively reducing the false‐positive rate of LDCT for lung cancer screening.

Keywords

• early diagnosis and early treatment
• lung cancer
• prediction model
• pulmonary nodules
• screening